Variability of cold season surface air temperature over northeastern China and its linkage with large-scale atmospheric circulations

2017 ◽  
Vol 132 (3-4) ◽  
pp. 1261-1273 ◽  
Author(s):  
Yuanhuang Zhuang ◽  
Jingyong Zhang ◽  
Lin Wang
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Surface Air Temperature ◽  
Cold Season ◽  
Northeastern China ◽  
Atmospheric Circulations

Linkages of surface air temperature variations over Central Asia with large-scale climate patterns

2021 ◽  
Author(s):  
Yuanhuang Zhuang ◽  
Jingyong Zhang ◽  
Lingyun Wu
Keyword(s):  
Central Asia ◽  
Air Temperature ◽  
Large Scale ◽  
Warm Season ◽  
Surface Air Temperature ◽  
Cold Season ◽  
Temperature Variations ◽  
The North ◽  
Climate Patterns ◽  
Air Temperature Variations

Abstract In this study, we investigate the dominant modes of surface air temperature variations of the cold season (from November through to the next March) and the warm season (from May to September) over Central Asia, and their associations with large-scale climate patterns for the period of 1979–2016. The first two modes of the cold-season surface air temperature (CSAT) over Central Asia, obtained by empirical orthogonal function (EOF) analysis, feature the mono-pole structure and the north-south dipole pattern, respectively. For the warm-season surface air temperature (WSAT), the leading two EOF modes are characterized by the homogenous structure and the northwest-southeast seesaw pattern, respectively. Further analysis indicates that the large-scale atmospheric circulation anomalies play key roles in the CSAT and WSAT variations over Central Asia. The CSAT variation over Central Asia is closely related with the Scandinavia pattern (SCAND), the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO), while the WSAT variation is tightly tied to the East Atlantic/Western Russia pattern (EAWR) and the NAO. These large-scale climate patterns tend to cause the CSAT and WSAT anomalies over Central Asia via their effects on regional geopotential heights, warming advections and other processes. Our findings are expected to facilitate the improvement of understanding and predicting the CSAT and WSAT variations over Central Asia.

scholarly journals Subseasonal relationship between Arctic and Eurasian surface air temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hye-Jin Kim ◽  
Seok-Woo Son ◽  
Woosok Moon ◽  
Jong-Seong Kug ◽  
Jaeyoung Hwang
Keyword(s):  
Air Temperature ◽  
Diabatic Heating ◽  
Surface Air Temperature ◽  
Significant Negative Correlation ◽  
Cold Season ◽  
Reanalysis Data ◽  
Necessary Condition ◽  
Sea Level Pressure ◽  
Blocking High ◽  
Pressure Anomaly

AbstractThe subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

scholarly journals Dynamical Adjustment of the Northern Hemisphere Surface Air Temperature Field: Methodology and Application to Observations*

2015 ◽  
Vol 28 (4) ◽  
pp. 1613-1629 ◽  
Author(s):  
Brian V. Smoliak ◽  
John M. Wallace ◽  
Pu Lin ◽  
Qiang Fu
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Cold Season ◽  
Reference Intervals ◽  
Pls Regression ◽  
Sea Level Pressure ◽  
Radiative Processes ◽  
Field Methodology ◽  
Hemisphere Surface ◽  
Circulation Changes

Abstract The influence of atmospheric circulation changes reflected in spontaneously occurring sea level pressure (SLP) anomalies upon surface air temperature (SAT) variability and trends is investigated using partial least squares (PLS) regression, a statistical method that seeks to maximally explain covariance between a predictand time series or field and a predictor field. Applying PLS regression in any one of the three variants described in this study (pointwise, PC-wise, and fieldwise), the method yields a dynamical adjustment to the observed NH SAT field that accounts for approximately 50% of the variance in monthly mean, cold season data. It is shown that PLS regression provides a more parsimonious and statistically robust dynamical adjustment than an adjustment method based on the leading principal components of the extratropical SLP field. The usefulness of dynamical adjustment is demonstrated by applying it to the attribution of cold season SAT trends in two reference intervals: 1965–2000 and 1920–2011. The adjustment is shown to reconcile much of the spatial structure and seasonal differences in the observed SAT trends. The dynamically adjusted SAT fields obtained from this analysis provide datasets capable of being analyzed for residual variability and trends associated with thermodynamic and radiative processes.

scholarly journals Urbanization effects on surface air temperature trends in Thailand during 1970-2019

2020 ◽  
Vol 26 (5) ◽  
pp. 200378-0
Author(s):  
Boonlue Kachenchart ◽  
Chaiyanan Kamlangkla ◽  
Nattapong Puttanapong ◽  
Atsamon Limsakul
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Urban Expansion ◽  
Great Influence ◽  
Surface Air Temperature ◽  
Urban Setting ◽  
Temperature Trends ◽  
Urban Warming ◽  
Wide Range ◽  
Increasing Trend

Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 <sup>o</sup>C per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thailand. The increasing trend of annual Tmean for Thailand as a whole after adjusting urban-warming bias is brought down to the same rate as the annual global mean temperature trend, reflecting a national baseline signal driven by large-scale anthropogenic-induced climate change. Our results provide a scientific reference for policy makers and urban planners to mitigate substantial fraction of the UHI warming.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

Trend of Surface Air Temperature in Eastern China and Associated Large-Scale Climate Variability over the Last 100 Years

2014 ◽  
Vol 27 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Ping Zhao ◽  
Phil Jones ◽  
Lijuan Cao ◽  
Zhongwei Yan ◽  
Shuyao Zha ◽  
...  
Keyword(s):  
Air Temperature ◽  
Large Scale ◽  
Eastern China ◽  
Surface Air Temperature ◽  
Tropical Indian Ocean ◽  
Population Data ◽  
Warming Trend ◽  
Climate Indices ◽  
The Past ◽  
Linear Trends

Abstract Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

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